I’m Siyanew

Abstract: Many platforms exploit collaborative tagging to provide their users with faster and more accurate results while searching or navigating. Tags can communicate different concepts such as the main features, technologies, functionality, and the goal of a software repository. Recently, GitHub has enabled users to annotate repositories with topic tags. It has also provided a set of featured topics, and their possible aliases, carefully curated with the help of the community. This creates the opportunity to use this initial seed of topics to automatically annotate all remaining repositories, by training models that recommend high-quality topic tags to developers.In this work, we study the application of multi-label classification techniques to predict software repositories’ topics. First, we augment GitHub’sfeatured topics. The core idea is to derive more information from projects’available documentation. Our data contains about 152K GitHub repositories and 228 featured topics. Then, we apply supervised models on repositories’textual information such as descriptions, README files, wiki pages, and filenames. We assess the performance of our approach both quantitatively and qualitatively. Our proposed model achieves [email protected] and LRAP of 0.890 and 0.805,respectively. Moreover, based on users’ assessment, our approach is highly capable of recommending correct and complete set of topics.

Keywords: Topic Tag Recommendation, Multi-label Classification, Recommender Systems, Mining Software Repositories, GitHub